ArticleNeuropsychiatric disease and treatment2025
A Clinical-Metabolic Prediction Model for Suicidal Behaviors Risk Stratification in First-Admission Major Depressive Disorder: A Cross-Sectional Analysis.
Article in Neuropsychiatric disease and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The clinical characteristics and biomarkers of suicidal behaviors (SB) in first-hospitalized patients with Major Depressive Disorder (MDD) remain poorly understood. This study aimed to investigate the prevalence, clinical correlates, and metabolic disturbances of SB in first-admission MDD patients in China, integrating psychosocial and biological markers to establish a predictive model. Methods: A cross-sectional analysis was conducted on 981 first-admission MDD inpatients. Sociodemographic data, clinical symptom severity (17-item Hamilton Depression Rating Scale [HAMD-17], 14-item Hamilton Anxiety Rating Scale [HAMA-14], PANSS positive subscale [PSS], Clinical Global Impression-Severity Index [CGI-SI]), and metabolic parameters (lipid profile, fasting glucose, thyroid function) were collected. SB was assessed using the Columbia-Suicide Severity Rating Scale (C-SSRS). Binary logistic regression and ROC analysis identified correlates and model performance. Results: The prevalence of SB was 13.46% (132/981). SB patients exhibited significantly higher psychotic symptoms, anxiety severity, and illness severity, along with elevated waist circumference (WC), diastolic blood pressure (DBP), total cholesterol (TC), and thyroid-stimulating hormone (TSH). Logistic regression identified HAMA (OR=1.72, 95% CI=1.25-2.37), PSS (OR=1.58, 95% CI=1.13-2.21), CGI-SI (OR=1.45, 95% CI=1.08-1.95), and TC (OR=1.32, 95% CI=1.04-1.68) as factors independently associated with SB (all Conclusion: SB in first-hospitalized MDD patients correlates with anxiety symptoms, psychotic features, and metabolic dysregulation. A multidimensional model integrating clinical and metabolic indicators is associated with high-risk individuals, supporting targeted prevention strategies.
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